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Abstract: Map showing the spatial distribution of ecosystems identified in Sierra Nevada high mountain (southern Spain). Several sources were used to generate spatial distribution of ecosystem types. The Land Cover and Use Information System of Spain (SIOSE)(https://www.siose.es/) was used as base spatial information. Other technical reports also supported the spatialization and identification of the ecosystem types (e.g. Molero-Mesa, J.; Pérez Raya, F.; López Nieto, J.M.; El Aallali, A. & Hita Fernández, J.A. (2001). Cartografía y evaluación de la vegetación del Parque Natural de Sierra Nevada [Mapping and assessment of the vegetation of the Sierra Nevada Nature Reserve]. Department of the Environment. Regional Government of Andalusia). The main ecosystem types identified were: natural forests (Holm oak and Pyrenean oak forests [Quercus ilex and Q. pyrenaica], Scot pine forests [Pinus sylvestris var. nevadensis]), high-mountain shrublands (Juniperus communis), pine plantations, high-mountain grasslands ("borreguiles) among others. The data generated is stored as a vectorial file (shapefile). The coordinate reference system is UTM Zone 30N WGS84) (epsg:23030). Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: EVENT LABEL: * LATITUDE START: 37.250000 * LONGITUDE START: -3.640000 * LATITUDE END: 36.910000 * LONGITUDE END: -2.590000 * LOCATION: Sierra Nevada, Spain * METHOD|DEVICE: Multiple investigations
Data Types:
  • File Set
Abstract: This dataset contains PISM simulation results (http://www.pism-docs.org) of the Antarctic Ice Sheet based on code release v1.0-paleo-ensemble (https://doi.org/10.5281/zenodo.3574033). PISM is the open-source Parallel Ice Sheet Model developed mainly at UAF, USA and PIK, Germany. With the help of added python scripts, all figures can be reproduced as in the journal publication: - Albrecht et al., "Glacial cycles simulation of the Antarctic Ice Sheet with PISM - Part 1: Boundary conditions and climatic forcing"; The Cryosphere (2020) --- Data: Find PISM results as netCDF data. See 'README.md' for a list of all performed experiment. All forcing input data for the experiments and plots can be downloaded and remapped via https://github.com/pism/pism-ais. Some of the original input data files are freely available, for others please contact the author or the corresponding data publisher. Figure plotting scripts (jupyter notebook based on python, see https://jupyter.org) in 'plot_scripts' access the uploaded PISM results in 'model_data' and save the plots to 'final_figures'. Jupyter notebook can be run in the browser and shared, see https://nbviewer.jupyter.org/url/www.pik-potsdam.de/~albrecht/notebooks/paleo_paper/paleo_paper_final.ipynb. --- Contact: Albrecht, Torsten (albrecht@pik-potdam.de) ; Potsdam-Institute for Climate Impact Research (PIK), Potsdam, Germany Category: geoscientificInformation Source: Supplement to: Albrecht, Torsten; Winkelmann, Ricarda; Levermann, Anders (accepted): Glacial cycles simulation of the Antarctic Ice Sheet with PISM - Part 1: Boundary conditions and climatic forcing. The Cryosphere, https://doi.org/10.5194/tc-2019-71 Supplemental Information: Not Availble Coverage: EVENT LABEL: * LATITUDE: -90.000000 * LONGITUDE: 0.000000
Data Types:
  • File Set
Abstract: This dataset contains PISM simulation results of the Antarctic Ice Sheet based on code release v1.0-paleo-ensemble (https://doi.org/10.5281/zenodo.3574033). PISM is the open-source Parallel Ice Sheet Model developed mainly at UAF, USA and PIK, Germany. See documentation in http://www.pism-docs.org. With the help of the added jupyter notebook (Python 2.7.3), all figures can be reproduced as published in the article: - Albrecht et al., "Glacial cycles simulation of the Antarctic Ice Sheet with PISM – Part 2: Parameter ensemble analysis", The Cryosphere (2020) --- Data: Find PISM results as netCDF data. See 'README.md' for a list of all performed experiment. All forcing input data for the experiments and plots can be downloaded and remapped via https://github.com/pism/pism-ais. Some of the original input data files are freely available, for others please contact the author or the corresponding data publisher. The jupyter notebook (https://jupyter.org) paleo_paper2_final.ipynb (based on python) in 'plot_scripts' accesses the uploaded PISM results in 'model_data' or 'supplement' and saves the plots as vector and pixel graphics to 'final_figures'. Edit header for changing work paths. Jupyter notebook can be run in the browser and shared, see https://nbviewer.jupyter.org/url/www.pik-potsdam.de/~albrecht/notebooks/paleo_paper/paleo_paper2_final.ipynb. --- Methods: The scoring scheme with respect to modern and paleo data based on Python 2.7.3 can be downloaded from (https://doi.org/10.5281/zenodo.3585118). The ensemble analysis calculates misfits to the paleo constraint database AntICEdat (Briggs & Tarasov, 2013) and to RAISED Consortium (2014) as well as to modern ice geometry from Bedmap2 (Fretwell et al., 2013), ice speed (Rignot et al., 2011) an GPS (Whitehouse et al., 2011). The analysis is based on Pollard et al., (2016) and Briggs et al., (2014). --- Contact : Albrecht, Torsten (albrecht@pik-potdam.de) ; Potsdam-Institute for Climate Impact Research (PIK), Potsdam, Germany Category: geoscientificInformation Source: Supplement to: Albrecht, Torsten; Winkelmann, Ricarda; Levermann, Anders (accepted): Glacial cycles simulation of the Antarctic Ice Sheet with PISM – Part 2: Parameter ensemble analysis. The Cryosphere, https://doi.org/10.5194/tc-2019-70 Supplemental Information: Not Availble Coverage: EVENT LABEL: * LATITUDE: -90.000000 * LONGITUDE: 0.000000
Data Types:
  • File Set
Abstract: Leads and pressure ridges are dominant features of the Arctic sea ice cover. Not only do they affect heat loss and surface drag, but also provide insight into the underlying physics of sea ice deformation. Due to their elongated shape they are referred as Linear Kinematic Features (LKFs). This data-set includes LKFs that were detected and tracked in sea ice deformation simulated in an Arctic configuration of MITgcm using a 2-km horizontal grid spacing and an active 5-class ice thickness distribution. The model data is sampled for the entire observing period of the RADARSAT Geophysical Processor System (RGPS). The data-set spans the winter month (November to May) from 1997 to 2008 and covers the entire Arctic Ocean. A detailed description of the model configuration and the data-set is provided in: Hutter, N. and Losch, M.: Feature-based comparison of sea-ice deformation in lead-resolving sea-ice simulations, The Cryosphere, https://doi.org/10.5194/tc-2019-88, accepted for publication, 2019. A detailed description of the algorithms deriving the data set is provided in: Hutter, N., Zampieri, L., and Losch, M.: Leads and ridges in Arctic sea ice from RGPS data and a new tracking algorithm, The Cryosphere, 13, 627-645, https://doi.org/10.5194/tc-13-627-2019, 2019. Category: geoscientificInformation Source: Not Available Supplemental Information: Data Description: The data set covers all RGPS winter data, i.e. November to May for the years 1996/97 to 2007/08. The LKFs of each winter season are saved in one TAB-delimited text-file (ASCII). In total the data-set contains in 12 files. In the csv-file each row corresponds to one pixel of an LKF in this year. In individual pixels are sorted by date and LKF. Each LKF gets a identifier number (LKF No.) that is unique in this winter. For track features the LKF No.(s) of parent LKF(s) from the previous RGPS time record are provided. The columns of the csv-files are structured in the following way: Start Year, Start Month, Start Day, End Year, End Month, End Day, Date(RGPS format), LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rate Specific comments: Start Year, Start Month, Start Day -> Start date of the RGPS time record in which LKFs are detected End Year, End Month, End Day -> End date of the RGPS time record in which LKFs are detected Date in original RGPS format -> RGPS format of date (first for digits are the year, the last three digits are the number of days). This format is used as filename by RGPS. LKF No. -> each LKF in one winter has its unique identifier number that can be used to track the feature Parent LKF No. -> LKF No. of the LKF from the previous time records, for that this LKF is a temporal continuation determined by the tracking algorithm. This entry can contain multiple numbers if the current LKF was formed from multiple LKFs. '0' is used as a fill value, if there is no parent LKF. ind_x,ind_y -> Indexes of the LKF pixel in original RGPS data that can be used to index original RGPS fields lon, lat -> position of LKF pixel divergence and shear rate -> The divergence and shear rate of RGPS deformation data at LKF pixel. The divergence rate can be used to distinguish leads and pressure ridges in the data-set, please see Hutter et al. (2019). Coverage: Not Available
Data Types:
  • File Set
Abstract: Leads and pressure ridges are dominant features of the Arctic sea ice cover. Not only do they affect heat loss and surface drag, but also provide insight into the underlying physics of sea ice deformation. Due to their elongated shape they are referred as Linear Kinematic Features (LKFs). This data-set includes LKFs that were detected and tracked in sea ice deformation simulated in an Arctic configuration of MITgcm using a 2-km horizontal grid spacing. The model data is sampled for the entire observing period of the RADARSAT Geophysical Processor System (RGPS). The data-set spans the winter month (November to May) from 1997 to 2008 and covers the entire Arctic Ocean. A detailed description of the model configuration and the data-set is provided in: Hutter, N. and Losch, M.: Feature-based comparison of sea-ice deformation in lead-resolving sea-ice simulations, The Cryosphere, https://doi.org/10.5194/tc-2019-88, accepted for publication, 2019. A detailed description of the algorithms deriving the data set is provided in: Hutter, N., Zampieri, L., and Losch, M.: Leads and ridges in Arctic sea ice from RGPS data and a new tracking algorithm, The Cryosphere, 13, 627-645, https://doi.org/10.5194/tc-13-627-2019, 2019. Category: geoscientificInformation Source: Not Available Supplemental Information: Data Description: The data set covers all RGPS winter data, i.e. November to May for the years 1996/97 to 2007/08. The LKFs of each winter season are saved in one TAB-delimited text-file (ASCII). In total the data-set contains in 12 files. In the csv-file each row corresponds to one pixel of an LKF in this year. In individual pixels are sorted by date and LKF. Each LKF gets a identifier number (LKF No.) that is unique in this winter. For track features the LKF No.(s) of parent LKF(s) from the previous RGPS time record are provided. The columns of the csv-files are structured in the following way: Start Year, Start Month, Start Day, End Year, End Month, End Day, Date(RGPS format), LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rate Specific comments: Start Year, Start Month, Start Day -> Start date of the RGPS time record in which LKFs are detected End Year, End Month, End Day -> End date of the RGPS time record in which LKFs are detected Date in original RGPS format -> RGPS format of date (first for digits are the year, the last three digits are the number of days). This format is used as filename by RGPS. LKF No. -> each LKF in one winter has its unique identifier number that can be used to track the feature Parent LKF No. -> LKF No. of the LKF from the previous time records, for that this LKF is a temporal continuation determined by the tracking algorithm. This entry can contain multiple numbers if the current LKF was formed from multiple LKFs. '0' is used as a fill value, if there is no parent LKF. ind_x,ind_y -> Indexes of the LKF pixel in original RGPS data that can be used to index original RGPS fields lon, lat -> position of LKF pixel divergence and shear rate -> The divergence and shear rate of RGPS deformation data at LKF pixel. The divergence rate can be used to distinguish leads and pressure ridges in the data-set, please see Hutter et al. (2019). Coverage: Not Available
Data Types:
  • File Set
Abstract: Coastal ecosystems are periodically exposed to short- and long-term hypoxia. Coastal organisms are thus exposed to these hypoxic conditions, though, many intertidal species are tolerant to this situation. The hypoxia tolerant species can endure hypoxia through metabolic rate depression. However, the effect of hypoxia and the following reoxygenation phase on the homeostasis of the intermediate metabolites are yet to be understood. In this study, we focused on the effects of 1 day and 6 days of hypoxia and 1 hour of reoxygenation after each hypoxic conditions on the homeostasis of the intermediate metabolites in the gill and helatopancreas tissue of two intertidal species, Mytilus edulis and Crassostrea gigas. According to our findings, the effect of hypoxia and reoxygenation on the intermediate metabolites in hypoxia tolerant C. gigas were (s)lower compared to the more sensitive M. edulis. The observed changes in multiple metabolic pathways were consistent with the higher resistance to oxidative injury during hypoxia-reoxygenation. Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: Not Available
Data Types:
  • File Set
Abstract: TEMPERSEA is a gridded temperature product for the Red Sea covering in the period 1958-2017 at monthly resolution. The product covers the Red Sea and the Gulf of Aden with a spatial resolution of 0.25°x 0.25° and 23 vertical levels. This product is based on a large number of in-situ observations collected in the region. After a specific quality control, a mapping algorithm has been applied to homogenize the data. Also, an estimate of the accuracy of the product has been generated to accurately define the uncertainties of the product. Category: geoscientificInformation Source: Supplement to: Agulles, Miguel; Jordà, Gabriel; Jones, Burt; Agustí, Susana; Duarte, Carlos M (2020): Temporal evolution of temperatures in the Red Sea and the Gulf of Aden based on in situ observations (1958-2017). Ocean Science, 16(1), 149-166, https://doi.org/10.5194/os-16-149-2020 Supplemental Information: Not Availble Coverage: EVENT LABEL: * LATITUDE: 12.300000 * LONGITUDE: 43.600000 * METHOD/DEVICE: Multiple investigations
Data Types:
  • File Set
Abstract: The identification and quantification of natural carbon (C) sinks is critical to global climate change mitigation efforts. Tropical coastal wetlands are considered important in this context, yet knowledge of their dynamics and quantitative data are still scarce. In order to quantify the C accumulation rate and understand how it is influenced by land use and climate change, a palaeoecological study was conducted in the mangrove-fringed Segara Anakan Lagoon (SAL) in Java, Indonesia. A sediment core was age-dated and analyzed for its pollen and spore, elemental and biogeochemical compositions. The results indicate that environmental dynamics in the SAL and its C accumulation over the past 400 years were controlled mainly by climate oscillations and anthropogenic activities. The interaction of these two factors changed the lagoon's sediment supply and salinity, which consequently altered the organic matter composition and deposition in the lagoon. Four phases with varying climates were identified. While autochthonous mangrove C was a significant contributor to carbon accumulation in SAL sediments throughout all four phases, varying admixtures of terrestrial C from the hinterland also contributed, with natural mixed forest C predominating in the early phases and agriculture soil C predominating in the later phases. In this context, climate-related precipitation changes are an overarching control, as surface water transport through rivers serves as the "delivery agent" for the outcomes of the anthropogenic impact in the catchment area into the lagoon. Amongst mangrove-dominated ecosystems globally, the SAL is one of the most effective C sinks due to high mangrove carbon input in combination with a high allochthonous carbon input from anthropogenically-enhanced sediment from the hinterland and increased preservation. Given the substantial C sequestration capacity of the SAL and other mangrove-fringed coastal lagoons, conservation and restoration of these ecosystems is vitally important for climate change mitigation. Category: geoscientificInformation Source: Supplement to: Hapsari, Kartika Anggi; Jennerjahn, Tim C; Lukas, Martin C; Karius, Volker; Behling, Hermann (2019): Intertwined effects of climate and land use change on environmental dynamics and carbon accumulation in a mangrove‐fringed coastal lagoon in Java, Indonesia. Global Change Biology, https://doi.org/10.1111/gcb.14926 Supplemental Information: Not Availble Coverage: Not Available
Data Types:
  • File Set
Abstract: Not Available Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: EVENT LABEL: * LATITUDE START: -33.906800 * LONGITUDE START: 18.433700 * LATITUDE END: -53.144700 * LONGITUDE END: -70.909100 * DATE/TIME START: 2018-12-15T00:00:00 * DATE/TIME END: 2019-02-07T00:00:00 * CAMPAIGN: PS117 * BASIS: Polarstern * DEVICE: Underway cruise track measurements
Data Types:
  • File Set
Abstract: Not Available Category: geoscientificInformation Source: Not Available Supplemental Information: Not Availble Coverage: EVENT LABEL: * LATITUDE START: -53.144700 * LONGITUDE START: -70.909100 * LATITUDE END: -53.144700 * LONGITUDE END: -70.909100 * DATE/TIME START: 2019-02-09T00:00:00 * DATE/TIME END: 2019-04-08T00:00:00 * CAMPAIGN: PS118 * BASIS: Polarstern * DEVICE: Underway cruise track measurements
Data Types:
  • File Set
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